Extension of the nu-SVM Range for Classification
Fernando Perez-Cruz, Jason Weston, Daniel Herrmann and Bernhard Schölkopf
In: Advances in Learning Theory: Methods, Models and Applications Nato Science Series. (III). (2003) IOS Press , pp. 179-196. ISBN 1586033417

Abstract

In this paper, we revisit how maximum margin classifiers can be obtained for a separable training data set, to also enable us construct ``hard'' margin classifiers for non-separable data sets. This can be achieved by finding the separation in which incorrectly classified points have the smallest {\sl negative} margin. This re-interpretation of the maximum margin classifier, when viewed as a soft margin formulation, will allow us to extend the range of SVM to any number of support vectors. We formulate the learning machine similarly to the nu-SVM, therefore we will be able to readily control the number of support vectors using the nu parameter.

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